Multi-channel communication platform with dynamic response goals

ABSTRACT

A multi-channel communication platform includes a messaging engine a routing engine and a queue manager. The messaging engine is operative to receive a plurality of textual messages from a user, the plurality of textual messages forming a message thread and including at least one query requesting a reply. The routing engine is operative to transmit the message thread to a message queue associated with a live agent and to receive a reply from the live agent and transmit the reply to the user. The queue manager is operative to provide the live agent with an adjustable reward following a response to the query, with the amount of the award being based on one or more of: the perceived complexity of the query; a number of outstanding or unassigned queries; or the perceived urgency of the query.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 17/503,637, filed on 18 Oct. 2021 and published as US2022/0038418, and which is a continuation of U.S. patent applicationSer. No. 16/888,616, filed on 29 May 2020 and issued as U.S. Pat. No.11,153,260, which claims the benefit of priority from U.S. ProvisionalPatent Application No. 62/855,917, filed on 31 May 2019. Eachapplication is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to a multi-channel communication platformfor managing communication and workflow.

BACKGROUND

Given the rise of mobile internet technology, the manner in whichindividuals and businesses communicate is rapidly changing. Unlike voicecommunication, where an individual is generally capable of maintainingonly one conversation at any given time, new forms of communication(e.g., SMS, MMS, IP based client messaging) enable users tosimultaneously converse with multiple different parties concurrently. Inaddition to being able to handle more concurrent conversations, thesenew means of communication each may have acceptable response times thatcan vary based on the urgency of the user. For example, an active userwith an urgent query may demand a prompt, near-real-time response.Conversely, a user with a casual question, sent via SMS, may set theirdevice down following the question and not check it again for a fewhours. In one study, where a standard web chat involved an average of 8minutes of handle time over an 8 minute contiguous block of time, an SMSchat involved a similar average of 8 minutes of handle time, thoughspread over 1.5 hours of sporadic contact.

The challenge of managing acceptable response time goals becomesparticularly problematic if an agent were asked to converse acrossmultiple messaging platforms, each with different customer expectationsof what constitutes an acceptable delay in receiving a response. Iflonger response times are permissible, staffing requirements may bereduced, which presents a cost savings to the company. As such, theshift to new forms of communication presents an opportunity to balancecustomer service response workloads that is not available with a livetelephone conversation.

SUMMARY

The present disclosure provides, among other things, a Robotic,Optimized Clienteling Communications platform (ROCCo) that can provide adynamic customer service experience between a consumer and anenterprise. The robotic nature can utilize machine learning and naturallanguage understanding to augment live agent abilities/responsivenesswhile also delivering a superior level of service to the consumer. Thesystem may permit clienteling at scale to produce unbreakable, long termrelationships while also enabling cross-platform communications that canpivot with real-time consumer demand. In a broad sense, the goal of thepresent system is to make conversation easy while providingrelationship-rich omnichannel communication and support at scale.

In some embodiments of the present disclosure, a multi-channelcommunication platform may include a messaging engine, a routing engine,and a queue manager. The messaging engine may be operative to receive aplurality of textual messages from a user through any of a plurality ofdifferent messaging services in communication with the messaging engine.The plurality of textual messages form a message thread and include atleast one query requesting a reply. The routing engine may then beoperative to determine a momentum of the plurality of textual messages.Finally, the queue manager may assign the message thread to a messagequeue associated with an agent. The message queue specifies a responsetime goal for providing the reply to the at least one query, where theresponse time goal is inversely proportional to the determined momentum.

Further extensions of the present disclosure provide systems that assista live agent in providing customer service to a user and/or in answeringa user's query. Such systems may include an automated agent with naturallanguage processing capabilities that provide the live agent withproposed response language for addressing the user's query. In someembodiments, the automated agent may populate a support window on a userinterface of the live agent with information about the user. Suchinformation may include, and without limitation, a product preference ofthe user, a size of the user, an indication of the user's recentbrowsing history, a summary of the user's product interests, a summaryof the user's spending patterns, a summary of prior message threads withthe user, a summary of user tagged products, or a summary of the user'spurchasing history.

Further, the automated agent may generate and display on the supportwindow, one or more hyperlinks to an internet webpage where a product isoffered for sale. Such links may be derived from the context of themessage thread between the user and the live agent. In one embodiment,the internet webpage may belong to a retailer, though may enableinventory from a third party to be purchased through the retailer'swebpage.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a multi-channel communication systemthat may be used to provide customer service and/or support to a user.

FIG. 2 is a schematic flow diagram for managing one or more conversationthreads in a multi-channel communication system, such as shown in FIG. 1.

FIG. 3 is a schematic graph of response time goals set as a function ofa conversation momentum distribution.

FIG. 4 is a schematic flow diagram for initially responding to a uservia an automated agent.

FIG. 5 is a schematic flow diagram for determining a momentum of aconversation.

FIG. 6 is a schematic table illustrating a queue of message threads thatmay be managed by a customer service agent.

FIG. 7 is a schematic display screen that may be presented to a customerservice agent for the purpose of managing a plurality of messagingthreads and for providing a prompt response to a user.

FIG. 8 is a schematic flow diagram illustrating a process forintegrating multiple customer support agents into a single conversationwith a user.

FIG. 9 is a schematic flow diagram illustrating a system for generatingan e-commerce link for making a product sale from a retailer or one of aplurality of third-party retailers.

FIG. 10 is a schematic flow diagram illustrating a system for creatingan elastic pool of live customer service agents for fielding inquiriesfrom a user population.

DETAILED DESCRIPTION

The following discussion and accompanying figures disclose amulti-channel communication system (e.g., system 10 of FIG. 1 ) that maybe used to provide customer service and/or support to a user. In manyembodiments, the user may be an end user and/or an existing orprospective customer. In other embodiments, however, the user may be aretail sales associate, where the system may respond to questionsregarding specific product information, style advice, and/or customproduct ordering.

As a general matter of terminology, when the present disclosurereferences a “user,” this is meant to refer to person who is seekingsupport. In a retail context, the user is generally an existing orprospective customer. Furthermore, the provider of information and/orthe person responding to the inquiry is referred to as the “agent” or“specialist.”

In some embodiments, this system may be configured to determine anappropriate response-time goal for an agent based upon theconversational speed of the user and/or the perceived urgency of theuser's message. In doing so, the present system differs fromconventional systems where response-time goals are based solely upon themessaging modality (e.g., an agent is capable of maintaining fiveconcurrent SMS threads).

The system may further operate to improve the quality and/or speed of aservice agent's response by analyzing the user's query and by routingthe thread or query to an agent or specialist that is most knowledgeableabout that particular subject. In instances where multiple queries ofdiffering subject matter are made in close proximity of each other, eachmay be separately routed to a different agent. To provide a better flowfor the conversation with the user, one agent may be deemed the primaryresponse agent, and all other secondary agents may feed their responsesto that primary agent for synthesis into the conversation.

The system may also support the agent's ability to provide relevantproduct or support information by coordinating the messaging client withthe user's account information. In this manner, the agent's display maypopulate with specific sales information (e.g., to provide shipment orreturn support), customer-specific preferences/sizes, purchasetendencies, or other such relevant information. In some embodiments,this ancillary information may be based on user account data, and insome embodiments it may be further refined by the context of thereceived message.

As shown in FIG. 1 , a multi-channel communication system 10 may includea messaging engine 22 in communication with one or more messagingservices 24, a thread management system 26, a routing engine 28, and aqueue manager 30. In some embodiments, the routing engine 28 may includean automated agent 32, and a momentum monitor 34.

The various messaging services 24 may typically be managed by externalproviders and may each operate on a different communication networks,utilize one or more different transport protocols, and/or communicatewith different endpoint devices. Examples of different messagingservices 24 may include a short message service (SMS), a multimediamessaging service (MMS), a push notification service, an internetprotocol (IP) messaging service, proprietary third party messagingservice, email service, and/or any suitable communication service. Eachof the messaging services 24 can include dedicated communication serviceinstances, channels, and different routing options.

The messaging engine 22 may include or may be operably connected with amessaging client that is configured to communicate over each or any ofthe connected messaging services 24. The messaging client may serve as afront end for communication via the messaging engine 22, and mayinclude, for example, a voice and/or text-based interface through whicha user may communicate. The messaging engine 22 may be used intransmitting messages, receiving messages, and managingsynchronous/asynchronous communication sessions via the messagingclient. In one configuration, the messaging engine 22 may be configuredto communicate using a plurality of different communication protocolsthat may be specific and/or utilized by the different messaging services24. For example, with an SMS message, the communication may rely on SMPPconnections to various service provider destinations; with an MMSmessage, the communication may rely on SMTP connections to variousservice provider destinations (for MM4) or alternatively they can relyon service resources accessed over HTTP/SOAP (for MM7). In many cases,the service provider responsible for a particular messaging service 24may provide an API that enables bidirectional communication with otherclients/users on that service.

FIG. 2 schematically illustrates a method of taking received messagefrom the messaging service 24 and building it into a message thread tobe passed to the message routing engine 28 as shown in FIG. 1 . Asgenerally illustrated, following the receipt of a message from amessaging service 24 (at 50), the messaging engine 22 may be configuredto extract both user identifying information and message content fromthat message (at 52). The user identifying information may be, forexample, a telephone number, a username, a user number, a device idnumber, or the like. In many cases, the user identifying information maybe encoded as part of the message and/or may precede the message in aheader block. Once extracted, this information may be passed to thethread management system 26 where the message content may be associatedto a user account maintained in a user database 36.

The thread management system 26 may generally be responsible formaintaining one or more ongoing message threads for each user,regardless of when the message is received. With continued reference toFIG. 2 , if the user identifying information from the message isrecognized from the user database (at 54), the thread management system26 may then check that user's account to determine whether an existingcommunication thread is open (at 56). If there is an existing openthread, the message content may be appended to that open thread (at 58).If no open thread exists, then the thread management system 26 maycreate a new thread (at 60) and pass that thread to the routing engine28 (at 62).

If the user identifying information is not recognized (at 54), either anew temporary account may be created, or the user may be prompted by areturn message (via the same messaging service 24) to provideidentifying information that may enable a user account to be accessed(at 64). In some instances, an orphan thread or created temporaryaccount may be migrated to a user's account later in the conversation,for example, once order specific details are provided to an agent. Asone example, if a user's message indicates that they wish to return anitem, the user identifying information may ultimately flow from themessage content, such as an order number that may be linked to a useraccount.

Referring again to FIG. 1 , the routing engine 28 may generally beresponsible for routing new message threads and new messages (includingany secondary information related to the message threads) to theappropriate live agent who may best support the user's query. As notedabove, in some embodiments, the routing engine 28 may include anautomated agent 32 or artificial intelligence that is capable ofunderstanding the nature and context of the message, and a momentummonitor 34 that is operative to determine the momentum of the messagethread. Knowing the nature and context of the message may enable therouting engine 28 to identify the appropriate agent (or pool ofqualified agents) to handle the query based on subject matter, technicalexpertise, or sensitivity of the request. Further, by knowing themomentum of the message thread, the system may be more adept atbalancing message loads between individual agents according to thesensitivity and/or message frequency of a particular thread.

The automated agent 32 may include a natural language response enginethat is capable of understanding the nature and content of the request.In some embodiments, the automated agent 32 may further include anartificial intelligence component that is operative to engage in apreliminary discussion with the user upon receipt of an initial message.For example, the automated agent 32 may address simple requests such asobtaining specific contact information, obtaining retail addresses,determining order status, and the like. Further, the automated agent 32may also serve the function of obtaining essential information that maybe required by a live agent. For example, if a return is requested, theautomated agent 32 may inquire about the specific product, the ordernumber, and/or the underlying reasons for the return.

In general, including an automated agent 32 in the initial messagereceipt may provide several benefits. First, it may dispose of simplerequests at a lower cost than if a live agent were required. Second, itmay gather information that may be required by the agent to answer aparticular query. In this manner, the live agent is not required toexpend response time/effort performing preliminary fact finding. Third,the automated agent 32 may engage with the user in a prompt manner toprovide the user with a sense of initial responsiveness even if a liveagent is not presently available or has a large queue of queries toanswer. This responsiveness may help prevent the creation or escalationof any user frustration. Finally, the back and forth with the automatedagent 32 may create a base message thread from which the momentummonitor 34 may determine the momentum of the conversation and setappropriate response time goals for the live agent.

The notion of conversation momentum recognizes that when conversing overtext-based messaging platforms, different users may message at differentrates and with different levels of urgency. A user who is engaged in asynchronous conversation (i.e., real time back and forth) may have ahigh velocity that may demand greater agent capacity (i.e., if the agentis engaged in real-time back and forth, then there is less capacity tohandle additional message threads with other users). Conversely, a userwho messages in an asynchronous manner may have a slow velocity (i.e.,the user may set their phone down for hours between text messageresponses, or only check email periodically throughout the day). Thisslower velocity may not require the same prompt response that would bedemanded in a synchronous conversation. As such, the slow velocity mayenable the agent to handle comparatively more message threads at anygiven period of time.

Similar to velocity, the urgency or mass of the query should also beaccounted for in determining the response goal. Extremely urgentrequests, or requests with a greater sense of user frustration or angermay require a more prompt agent response to avoid unnecessarilyescalating the concern. Conversely, a casual request (e.g., is thereanything new coming out this weekend) may have a comparatively lowermass that does not require the same level of prompt attention. In oneembodiment, a conversational mass may be determined by a naturallanguage processing algorithm that may aim to understand the nature ofthe request together with the level of emotion conveyed by the thread.

In some embodiments, a conversation's mass may further be influenced byone or more attributes of the user's account. For example, a messagefrom a retail store employee may be assigned a higher mass than a userwith no prior experience with the company. Similarly, other factors suchas sales history, social media exposure, public influence, companyengagement, and/or customer demographics may influence mass. Forexample, a celebrity with high social media exposure, high publicinfluence, and a corporate sponsorship from the company may have agreater mass than a customer who has a minimal sales history and nosocial media exposure. While the mass should not affect the care withwhich the user is handled, it can be used to adjust an agent's queuesize or the number of other message threads the agent may handleconcurrently with that request. In this example, the celebrity mayconsume an agent's entire queue such that that agent may handle no othermessage threads. Likewise, the sporadic consumer may be assigned to anagent who is managing multiple message threads concurrently.

In one embodiment, a conversation's mass may be scored on a 0-100 scaleby the automated agent 32. This scoring may a percentile-like scoring,where a 0 has absolutely no urgency or emotion and a 100 is pure urgencyand pure emotion (i.e., both numbers being theoretically unobtainable).Each message or collection of messages may then be scored according totheir use of expressive or inflammatory words, phrases or context thatconnotes a time-sensitive inquiry, or the like. Message velocity may bescored, for example, as the frequency of the received message (i.e., theinverse of the instantaneous or average period between messages).Conversation momentum may therefore equal the relative, scored mass,multiplied by the average frequency.

It has been found that a histogram of conversation momentum 65 generallytakes the form of a normal distribution/bell curve, such as shown inFIG. 3 (i.e., with conversation momentum being represented on thehorizontal x-axis, and frequency on the y-axis). In some embodiments,the momentum distribution 65 may be used to set response time goals 66(represented on the secondary y-axis). For example, a conversation withmomentum in the lowest decile 68 (i.e., lowest momentum constitutingcomparatively slower messaging velocity and comparatively lower urgency)may have a response time goal set at a first value 70, such as 15minutes. Conversely, a conversation with a momentum in the highestdecile 72, (i.e., greatest momentum constituting comparatively greatermessaging velocity and comparatively greater urgency) may have aresponse time goal set at a second value 74, such as 15 seconds.Finally, a conversation with a momentum at the median 76 on the momentumdistribution may have a response time goal set at a third value 78, suchas 30 seconds. In one configuration, such as illustrated via the examplegoals, the response time goal function across the distribution may bynon-linear. This is because differences in user expectations on theupper end of the distribution have been found to only marginally vary,whereas acceptable response times on the lower end of the distributionare more heavily influenced by a user's sporadic conversation pattern,which affords a significantly greater response time. As such, theapplicable response time goal may vary inversely, though non-linearlywith the determined momentum. In another embodiment, not shown, theapplicable response time goal may vary inversely and linearly with thedetermined momentum.

Referring again to FIG. 1 , in one configuration, the momentum monitor34 may analyze the preliminary conversation between the user and theautomated agent 32 to assess the mass of the query along with thevelocity at which the user converses. Quantitative measures of these twofactors may then be combined to form a measure of momentum which will beused to dynamically set agent response-time goals, adjust the size ofthe agent's queue, and allow the agent to prioritize which pendingqueries should be responded to first.

FIG. 4 schematically illustrates a method of initially responding to auser query, such as may be performed by the automated agent 32. Asshown, once the message thread (or some indication that a new thread hasbeen opened) has been passed to the routing engine 28, the automatedagent 32 may review the initial message (at 80) using one or morenatural language algorithms to determine the nature and urgency of therequest. The automated agent 32 may then respond to the user (at 82)with a greeting, a recognition of the user's request, and either ananswer to the user's request or a follow up question. Upon receipt of asubsequent message from the user, the automated agent 32 may determine(at 84) whether the prior response satisfied the user's query. If so,then the automated agent 32 may close the thread (at 86). If not, theagent 32 may either follow up with the user (at 88) or pass the threadto the momentum monitor 34 (at 90).

FIG. 5 schematically illustrates one manner of operation for themomentum monitor 34. As shown, the momentum monitor 34 may review thethread to determine a conversation velocity (at 92) based on the averagetime for the user to respond (i.e., to either begin or send theresponse). In one embodiment, the velocity may reflect a total averageof all response times. In another embodiment, the velocity may reflect asimple or exponential moving average (which may weight more recent userresponse times greater than initial user response times. The momentummonitor 34 may then make an assessment of the mass of the conversation(at 94) based upon at least one of: a natural language assessment of theemotion of the messages (e.g., strong positive to strong negative)and/or the nature of the request (e.g., casual to urgent); or a usercharacteristic, attribute, relationship or engagement with the company,sales history, public influence, social media presence, and/or the like.

From the calculated velocity and determined mass, the momentum monitor34 may then compute a momentum score (at 96) that reflects thecombination of the two parameters. This momentum score, together withany available conversation context from the automated agent, may then beused by the routing engine 28 to dynamically assign the message threadto an available live agent. Once assigned, the thread may be passed tothe queue manager 30 (at 98) to supervise timeliness of responses withineach user's respective queue.

Referring again to FIG. 1 , the present system may generally include aplurality of live agents (A₁ through A_(n)), each having their ownrespective message queue (Q₁ through Q_(n)) that they are responsiblefor answering/managing. In general, the message queue may comprise a oneor more conversation threads that a live agent is responding to at anygiven time. FIG. 6 schematically illustrates an example of a messagequeue 38 that comprises a plurality of open message threads 100, eachhaving a determined thread momentum 102. Each live agent may have apredefined maximum response capacity 104 that is representative of theirskill and throughput as an agent. The routing engine 28, in combinationwith the queue manager 30 should operate such that the sum of the threadmomentum 102 in a live agent's queue 38 should not exceed the maximumresponse capacity 104 for that agent. The queue 38 may further representthe agent's available capacity 106, which is that agent's maximumcapacity 106 minus the sum of the thread momentum 102 within the queue38.

In one embodiment, the routing engine 28 may maintain one or moreunassigned message threads that have been assigned a respective threadmomentum 102 though are not yet assigned to a live agent. In such acase, the routing engine 28 may first filter the pool of total agents toidentify a subset that have the technical and/or customer serviceexpertise to handle the inquiry as best understood by the automatedagent 32. The routing engine 28 may then consult the queue manager 30 todetermine which agents in this subset are both accepting new threads andhave available capacity to handle the unassigned thread withoutexceeding their available capacity 106. In one embodiment, once assignedby the routing engine 28, the queue manager 30 may then transfer theunassigned thread 108 to an agent's queue 38 and update the availablecapacity. In another configuration, live agents may be able to view thetotal pool of unassigned message threads for which they have therelevant expertise to answer, and may be able to then select or opt-into conversations.

Once the message thread is loaded in an agent's queue 38, the queuemanager 30 may operate to assign a response-time goal 112 to the thread100 based on the thread's determined momentum score/value. In general, aresponse-time goal is a target maximum time that the agent allotted torespond to an outstanding user query. Response-time goals may varyinversely with the momentum value such that high momentum threads willhave comparatively shorter response-time goals than lower momentumthreads. In one embodiment, each thread 100 may further reflect theamount of time remaining 114 in a given response window. Thistime-remaining metric 114 may reflect the response-time goal minus theelapsed time since the last message was received from the user in thatthread 100. This time-remaining metric 114 may resemble a countdowntimer that indicates to the respective agent 36 how much time is stillavailable to respond.

In one embodiment, the queue manager 30 may be configured to interveneif the time-remaining metric 114 reaches zero (i.e., the entireresponse-time window elapses) and the agent has not yet begun typing aresponse. Such an intervention may involve the queue manager 30 sendinga message to the user to indicate that the agent is looking into thequestion and needs additional time. In one configuration, thisintervention may be worded as if the agent personally sent the message.In one embodiment, this interventional message may start a secondarytimer, such as shown as a negative number associated with “USER D” inFIG. 6 , which may then be monitored for performance evaluation purposesand/or to adjust the agent's maximum response capacity 104.

Referring again to FIG. 1 , once a response is provided by an agent toan outstanding inquiry, it is fed back to the thread management system26, where the agent response is appended to the end of the messagethread associated with the user's account. From there, the message ispassed to the message engine 26, where it is transmitted back to theuser via one or more of the messaging services 24. In one embodiment,because the present system operates independently of how the message isreceived and/or sent, some or all of the message thread may be madeavailable across different messaging services. For example, a user maybegin a conversation with an agent over SMS messaging while on the go,and then switch to an IP app when they arrive home. In such an example,the messaging engine 22 may make the entire message thread (or at leasta predetermined number of the most recent messages) available via the IPapp.

In some embodiments, the routing engine 28 may be persistent and maymonitor each incoming message as it is appended to a message thread. Forexample, in some embodiments, the momentum monitor 34 may continuouslyupdate the momentum of a conversation, for example, using simple orexponential moving averages, or using other weighting schemes that mayassign more recent momentum calculations with a greater weight than moredistant ones. In this manner, a user who wishes to engage in asynchronous conversation after a period of extended delay may have theirmessage thread more quickly assigned with faster response-time goals. Insome embodiments, if an updated momentum for a particular thread 100causes the sum of the thread momentum 102 in an agent's queue 38 toexceed the agent's maximum response capacity 104, then the routingengine 28 may automatically, or upon confirmation by the live agent,reassign a particular thread (e.g., the thread having the renewed userinterest, or a thread of comparatively lower mass or momentum) to adifferent live agent. In another embodiment, if the sum of the threadmomentum 102 only marginally exceeds the agent's maximum responsecapacity 104 (i.e., exceeds by less than a predetermined amount), thenthe routing engine 28 may simply prevent additional threads from beingadded to the queue, or may take such into consideration when assessingagent performance/responsiveness. Still further, in some embodiments, abuffer capacity may be maintained between the sum of the thread momentum102 and the agent's maximum response capacity 104 so that fluctuationsin momentum may be accommodated without exceeding the agent's maximumcapacity.

In addition to continuous thread momentum updating, in some embodiments,the routing engine 28/automated agent 32 may continuously evaluate theuser messages upon receipt using a natural language processing algorithmto assist the live agent providing support. More specifically, theautomated agent 32 may analyze each received user message individuallyand/or in the context of the broader thread for the purpose ofgenerating ancillary information that may be beneficial to the liveagent and may be forwarded to the live agent together with the newlyreceived message. Such ancillary information may include, for example,potential answers to the user's query, template response language,relevant product information; relevant order information, and the like.In the case of proposed response language, the live agent may have theultimate discretion of whether to include the proposed language in aresponse, to modify/supplement the proposed language in a response, orto ignore the proposed language altogether. The live agent's ultimateresponse may then be used to train the machine learning models of theautomated agent so that it may provide improved and/or more relevantsuggestions in the future.

FIG. 7 schematically illustrates one example of a screen/interface 140that may be presented to a live agent. As generally shown, the interface140 includes a representation of the agent's queue 38 (similar to whatis shown in FIG. 6 ), an active chat thread 142 for interacting with auser, and a support window 144 where received ancillary information(from the automated agent 32) may be displayed. The agent's queue 38 mayinclude a plurality of active threads 100, which include a firstplurality of active threads 146 that are awaiting an agent reply, and asecond plurality of active threads 148 that have been replied to and areawaiting a user follow-up. Each thread 100 may include an associatedname 150 (e.g., a username or name of the user), a message statusidentifier 152, and a time-remaining metric 114. Clicking on, orotherwise selecting one of the threads 100 then populates the chatthread 142 with the historic and on-going message thread between theagent/company and the user. Further selecting one of the threads 100 mayalso call up any recently provided ancillary information about the userand/or user's inquiry in the support window 144.

As generally illustrated in FIG. 7 , the chat thread 142 may comprise amixture of user messages/queries to the agent/company 160, messages 162from the automated agent 32 to the live agent (e.g., facilitating ahandoff), auto-generated responses 164 to the user from/on behalf of thelive agent, and messages from the live agent to the user.

During the chat, the support window 144 may serve as the agent's primarysource of information about the user and the user's query. Byconsolidating this information in a single window/region of the display,the present system may permit the agent to respond more promptly to theuser. In some embodiments, the support window 144 may include a userattribute section 170 and a proposed response section 172. The userattribute section 170 may provide the agent with acquired insight aboutthe user's interests, preferences, purchasing history, and the like,while the proposed response section 172 may include potential responselanguage provided by the automated agent 32 (via a natural languageprocessing algorithm) in an effort to guide or streamline the liveagent's response.

In some embodiments, the user attribute section may include, forexample, user profile information 180, user history information 182,user account information 184. The profile information 180 may include,for example, a brief summary of: the user 190; the user's productpreferences 192 (e.g., typical products, styles, and/or colors that theuser may purchase); the user's recent browsing history 194; the user'sinterests; the user's engagement with mobile apps or web pages; theuser's spending patterns, and/or the user's contact information. Historyinformation 182 (i.e., which may populate once the tab header isselected) may include, for example, a summary of prior conversationswith other agents, user social media engagement, tagged products, sharedproducts, prior agent product suggestions, and the like. Finally,account information 184 may include a summary of prior user orders,ordered products, favorited products, product offers sent to the user,discount offers sent to the user, and the like.

The present system may further be configured to utilize multiplespecialized live agents within the course of a single chat to providethe best quality answers to a user. For example, if during the course ofone conversation/query, a user makes an unrelated request, prior systemsmay have required the agent to complete the current conversation andthen transfer the conversation to a second agent that may assist on theunrelated request. In the present system, the secondary agent may becomean assistant to the primary agent who is maintaining the activeconversation with the user. In this manner, the entire conversation isseamless from the user's perspective, while involving multiplespecialists on the agent side.

FIG. 8 provides a flow diagram of a process 200 of integrating the inputfrom a plurality of agents into a support conversation while providing aseamless experience for the user. As generally illustrated, the processbegins at 202 when the routing engine 28 detects one or more questionsin a question thread. This may occur using one or more natural languageprocessing algorithms that continuously monitor the incoming messageflow. In one embodiment, the one or more questions may be containedwithin a single user message. In another embodiment, the one or morequestions may be divided across a plurality of messages from a user,which may come in direct succession, or may have intervening agentresponses.

Once the various questions have been identified at 202, each questionmay be coded as an object within software (at 204), and may be assigneda general topic or category. The system may then examine whether or notthe user has an existing thread open (at 206). If not, then the routingengine 28 may begin by identifying a primary question/query at 208. Theprimary question may be the question that is determined to have thegreatest mass or urgency, the greatest probability for escalation,and/or that may be answered by the broadest number of agents (accordingto predetermined agent scoring that is indicative of the agent's levelof specialization, and their level of sophistication). In a generalsense, knowledgeable, specialized agents (e.g. Olympic athletes) aremore expensive resources than less knowledgeable agents. With continuedreference to FIG. 8 , once the primary question is determined (at 208)an agent with suitable experience for that question and with availablecapacity may be assigned as the primary agent (at 210).

If or when an agent is assigned to the thread, the process 200 may theninclude passing the received and analyzed message to that agent (at212). The assigned primary agent may then be prompted or alerted to theexistence of different questions/objects within the message or thread(at 214). In one configuration, the primary agent may select one of thenon-primary objects (i.e., the secondary questions/objects) and elect totransfer that secondary question to a secondary agent (at 216). Thesecondary agent may then answer the question and the response may beforwarded back to the primary agent (at 218) for potential inclusioninto the conversation. By prompting the primary agent initially, thisprocess enables the primary agent to answer questions that he/she feelscomfortable with, while referring out those questions that are moredifficult or that require more specialized knowledge.

In one configuration, when the object is created (at 204), the routingengine 28 may determine one or more topic categories that the query mayfall within. These categories may be pre-configured to match withexisting departments/specialty areas and/or may be organically developedto provide system flexibility. Regardless, when the primary agent isprompted (at 214) to hand off the secondary questions, the prompt mayinclude a list of available live agents who are a suitable fit for thedetermined category. Alternatively the agent selection may occur outsideof the primary agent's view, though may still be based on suitabilityfor the determined topic category for the query. In still otherembodiments, the primary agent may flag a topic for assistance, whichmay send the query back to the broader pool of live agents where otheravailable agents may select the query or otherwise opt in to thesecondary conversation. Response time targets for the second agent maybe dictated by the response time target for the primary conversation.More specifically, the response time goal for the second agent may besmaller than the response time goal for the primary agent. In oneconfiguration, it may be a fixed duration shorter than the primaryresponse time target.

Referring again to FIG. 7 , the support window 144 may further assistthe agent by providing one or more deep links to specific product pageswhere a user can purchase the referenced product (i.e., links similar towhat is shown as the user's browsing history 194). These links may bepopulated by the automated agent 32 and are easily copied to the chatwindow by the live agent. FIG. 9 schematically illustrates oneembodiment of a system/method 250 that may be used to generate relevantdeep links to specific product pages where a user can purchase asuggested product. As shown, the automated agent 32 may begin byassembling a database 252 of available inventory. This database 252 mayidentify some or all of the product inventory maintained by theretailer's brink and mortar stores 256, the retailer's e-commercebusiness 258, one or more third-party brick and mortar stores 260 (e.g.,stores located proximate to the user), and/or one or more third-partye-commerce businesses 262. As used herein, the database 252 may be apersistent database that updates periodically, or it may be generatedon-demand by the automated agent 32 polling one or more of thestores/businesses 256, 258, 260, 262 following the identification of asuggested product.

The link creation process may generally begin upon receiving a productrequest 264 from the user or live agent, or when the automated agent 32recognizes a user's product history or is able to identify a specificproduct (266) from the context of the discussion between the user andthe agent. Once this occurs, the automated agent 32 may consult thedatabase 252 to determine if the product is available from the retaileror from a participating third party. If the agent 32 determines that theproduct is in stock, it may then generate a link 270 to a website wherethe product may then be purchased. This link 270 may be transmitted tothe live agent and displayed on the support window 144, where the liveagent may copy it into a chat interface 272.

When linking to specific product, or making product available forpurchase, the automated agent 32 may generally be configured to givepreference to product inventory that is maintained by the retailer ifall other factors are equal. In situations where the user's requestedtimeline cannot be satisfied by the retailer, however, then the timelinemay dictate referring to third-party inventory. For example, if the userrequests same-day pick up, the automated agent 32 may prioritize brickand mortar stores over e-commerce sites, even if that means linking to athird party rather than the retailer's own inventory. In this manner,knowledge of the user's location as well as the location of thepertinent inventory is important in creating the most on-point productlink. With this knowledge of third-party inventory, the live agent maybe a single source of information on product availability, and the useof the automated agent 32 in this supporting role would eliminate theneed for either the user or the live agent from having to scourthird-party websites to determine availability.

Regardless of where the product originates from, in one configuration,the link 270 may direct the user to a front-end user interface, such asan internet app or product website 280 maintained by the retailer, whichmay serve as the sales interface for all purchases. More specifically,the link 270 may direct the user to an app or product website wherelocal pickup or shipped options are both available (should the availableinventory support this). If a sale is completed through the front-enduser interface for local pickup at a local third party retailer, thenthe automated agent 32 (or other associated e-commerce system) wouldcoordinate the purchase with that retailer while instructing the thirdparty to hold the product for pickup. In other embodiments, the link 270may direct the user to third party websites for direct purchase throughthose sites.

Referring again to FIG. 7 , the user interface for the agent maydynamically change based on the consumer they are assisting. Forexample, the interface would only provide relevant product searcheswhere the consumer is located (nearby inventory availability), switchlanguage spell checker based on preferred consumer language, price willbe in the correct currency of the consumer, etc. This could then enableagents from all around the world to assist any customer as long as theycan speak/write the same language as the consumer they are assisting.For example, if a live agent in the US was assisting a consumer inChina, the support window 144 would switch to Chinese relevant responseinfo, have access to inventory in China stores, and all product priceswould be in RBM.

FIG. 10 schematically illustrates a system level architecture 300 thatmay employ a plurality of live agents 302 (e.g, Agents A₁ through A_(n)in FIG. 1 ) to respond to inquiries/communications 304 from a userpopulation 306. In one configuration, the plurality of live agents 302may comprise a plurality of centralized live agents 308 (Agent₁ throughAgent_(N)) and/or a plurality of distributed live agents 310 (Agent_(D1)through Agent_(DN)). Alternatively, this plurality of live agents 302may comprise a plurality of dedicated live agents 308 (i.e., wherecustomer service is their primary job responsibility even if theyphysically work in a decentralized manner) and/or a plurality ofon-demand live agents 310. In general, the centralized/dedicated liveagents 308 may be a group of agents who are all primarily tasked withproviding customer service to the user population 306. In oneconfiguration all of these agents may be located at a common callcenter. Conversely, the distributed/on-demand live agents 310 mayinclude individuals who are operating in a more individualized ordistributed capacity, or who have the ability to flex time fromdifferent responsibilities to the purpose of providing customer support.In one embodiment, these distributed/on-demand live agents 310 mayinclude retail associates from brick and mortar retail stores who haveavailable time during slow retail periods. Additionally, in someembodiments, these distributed/on-demand live agents 310 may includeenthusiasts who are knowledgeable about the particular industry, thoughare not directly employed in a retail capacity (e.g., a competitiveathlete supporting other athletes, employees of the company innon-retail or consumer facing roles, and the like).

To encourage an elastic agent pool, particularly among thedistributed/on-demand live agents 310, one or more rewards 312 may beprovided for responding to the needs of the user population 306 and forsupporting the business's overall objectives. In one configuration, themagnitude of the reward 312 may be scaled via a supply/demand-pricingmodule 314 to adjust the reward according to one or more inquiry-pricingmetrics 316 and/or the size 318 of the agent population that isqualified to handle the inquiry. Such inquiry-pricing metrics 316 mayinclude the technical complexity of the request, the total number ofoutstanding/unassigned user inquiries, and/or the mass of the particularrequest. More complex inquiries, and higher mass inquiries may dictatelarger rewards, as would a higher quantity of unassigned inquiries.

In one embodiment, live agents 302 may each be scored based on relevanttechnical expertise and/or expertise in their capacity as an agent,where their relative score or ability may qualify them for handlingcertain inquiries. For example, in an athletics context, a collegiateathlete would be deemed to have greater technical expertise in theirsport than a high school athlete, though may have considerably lessexpertise in a different sport. Likewise, a career customer service (CS)specialist may have greater expertise in handling sensitiveinquiries/customer concerns than an athlete with little formal CSexperience. Further, a career CS agent for a particular sport may havedeveloped more technical expertise in that sport than an amateurathlete, even if the CS agent does not play that sport personally. Inone embodiment, expertise may be conveyed either as an experience point(XP) value, or as a level, where a certain amount of experience wouldthen be required to advance to the next level.

With continued reference to FIG. 10 , each user inquiry 304 may beevaluated by the automated agent 32 using its natural languageprocessing to determine the inquiry's emotional intensity and requiredtechnical expertise. This evaluation may then adjust an agent filter 320to limit the population of available agents to only agents with therelevant technical and customer service expertise (e.g., scores). Inthis manner, a marathon runner who has an urgent pre-race inquiry mayhave his/her query filtered so that only agent's with a certain level ofrunning experience and a proven level of immediate responsiveness/issueresolution abilities may see the inquiry.

Following this filtering, and provided that the mass of the newconversation does not exceed the agent's available pre-determinedcapacity, available agents may opt-in to conversations/inquiries andestablish a chat session 322 with the user. Each chat session 322 may becontinuously evaluated by a reward adjustment module 324 according to avariety of dispute resolution and business promotion metrics. In thisembodiment, positive dispute resolution outcomes and advancement ofbusiness promotion goals may lead to a positive reward adjustment,whereas dispute escalation and anti-business statements may lead to anegative reward adjustment. Examples of positive business practices thatshould be rewarded include, for example, signing users up for newcustomer accounts, encouraging users to log into existing accounts,establishing a relationship with the consumer such that the inquiry orcontact occurs following the resolution of an initial inquiry, gettingusers to click through links to product pages, and making a sale.Examples of negative business practices that should be penalizedinclude, for example, making disparaging comments about the company,product, or consumer. Similarly, dispute resolution outcomes may bebased, for example, off of customer satisfaction surveys following thesession.

Once the inquiry is resolved and the reward value is determined, thereward may be credited to an account belonging to the agent. In oneembodiment, the reward may have a direct monetary value and may beexchanged for cash, merchandise, and/or discounts via a reward store326. In another embodiment, the reward may be a token or credit that hasno direct monetary value, though may be exchanged for one or moredigital items or in a gacha-style game to receive a loot/prize box viathe store 326. This loot box may contain one or more digital items ofvarying scarcity that, for example, may be used to adorn the user'saccount and/or be displayed in conjunction with an avatar belonging tothe user. In one embodiment, the gacha-style game may enable the agentto receive one or more digital collectables, which may be uniquelystored/identified/validated on a blockchain ledger (i.e., where thedigital collectable directly corresponds with a token stored on adistributed blockchain ledger) to ensure their scarcity. In a furtherextension, this digital collectable may be capable of being used in oneor more video game applications to improve an attribute of the user'scharacter or gameplay, such as described in U.S. patent application Ser.No. 16/707,741, filed 9 Dec. 2019 which is incorporated by reference inits entirety and for all that it discloses.

In a further extension of the architecture shown in FIG. 10 , in oneembodiment, each agent may have an avatar that may act as the forwardfacing image of the agent to the user (such as illustrated in the upperleft corner of the user interface shown in FIG. 7 ). In one embodiment,one or more attributes of the avatar may evolve or exist according tothe agent's scored technical expertise. For example, as the agentbecomes more knowledgeable about running, the avatar may acquiredifferent or more scarce running shoes. In some embodiments, the agent'sselection of avatar embellishments (clothes, accessories, activities,scenes, etc.) may provide insight about the agent's interests, which mayfurther serve to aid in routing a user's inquiry.

In still another embodiment, the present system may be used to assist auser in designing a custom article (e.g., shoe or article of apparel),for example, via a web interface such as the NIKEID® or NIKE BY YOU®systems, commercialized by Nike Inc. If the user needs assistance orinspiration, he/she may have the option of connecting with a live agentvia the present system. Likewise, if the user dwells on a particulardesign choice for more than a preset amount of time, or alters theirselection more than a predetermined number of times, the automated agent32 may see that the consumer is struggling and may automatically notifya design agent to assist. The connected live agent may then have theoption of chat with the consumer to assist in designing, take over theirUI control, or open up a video chat session to further engage with theuser. Additionally, as the user is designing the article, the userinterface/experience can be recorded, which may then be turned into atime lapse video to be shared by the consumer to their social network orit can be used by the agent as a record to better assist the consumernext time. In some embodiments, data may be extracted from video tolearn more about the consumer and how to improve the creation experiencefor subsequent users.

While the disclosure provided above is primarily focused on aretail/customer service context, this example of the currentfunctionality should not be interpreted to unnecessarily constrain thepresent technology. Instead, it is an example of the present technologybeing used to facilitate business-consumer communications. In otherexamples, however, the present technology may be used to managecommunications between companies (e.g., a supply base communicating withan OEM), or even between individuals within a single organization (e.g.,a manager and his/her team).

In these business-business examples, the manner of communication isoften not much different than in the retail/customer support context.Employees, project teams, and suppliers, are finding new ways tocommunicate, including SMS/MMS messaging, IP based chat systems, andother such means of communication. While explicit response times may notapply to the same extent, the notion of conversational momentum may beused in a similar manner to manage an individual's response priorities(i.e., a supervisor synchronously texting may have both a high velocityand a high mass, whereas an office manager seeking an RSVP for a pizzalunch over email may have a low mass and a low velocity). Similarnotions of automated responses may likewise apply in an intrabusiness orinterbusiness context.

In a further extension of the present system, because the routing engine28 and automated agent 32 may be persistent and may process each messageas it is received, the present system is particularly well suited fordetermining macro-level issues and trends. Furthermore, because thesystem understands user characteristics, it may be configured toidentify the demographics, interests, and/or geographical areas that aredriving the issues/trends. In either instance, this real-time info wouldallow the company to pivot to better meet the needs and/or demands ofthe consumer. (e.g., tweak messaging to head off an issue, or alterdevelopment/product plans to more quickly respond to the trend.Similarly, the agent 32 may be well positioned to assess user responseand satisfaction to specific answer types (i.e., prior to the engagementof the live agent). With this macro-level understanding, the automatedagent 32 may adapt its response strategy over time to drive toward agreater sense of user satisfaction. Additionally, the automated agent 32may use actual live agent responses to the user as training data toimprove its ability to directly respond to the user and/or to improveits ability to provide meaningful suggested responses to the live agent.This training data may further be weighted according to the live agent'srelative rank or customer service score, where the responses of moreskilled agents would have a greater weight in the training data set thanresponses of less skilled agents.

In one embodiment, the automated agent 32 may have a defined personalityfor purpose of generating natural language responses with a suitabletone. For example, if the present system were used to support anathletics apparel/equipment brand, the automated agent 32 may beconstructed to resemble: a 25-30 year old post-collegiate D1 athlete(e.g., All American runner); an athlete at the DNA level, but a hypebeast in the office; an avid supporter/ambassador of the brand; someonewho cops exclusive sneaker drops, but believes that you should wear yourkicks from social media to the field of sport; someone with seriousopinions about who the GOAT is and what the best meal is after a hardworkout; quick witted, a little sassy, and a pro at culturing a longterm inside joke; a friend that will never talk down to you, though issuper passionate and very confident (minus the arrogance); causal, butnot over the top; and someone who enjoys celebrating the higher love.Other personality types may also be used. Finally, while the automatedagent may have a name, such as Eugene, those who know it well may simplyrefer to it as Rocco.

Aspects of this disclosure may be implemented, in some embodiments,through a computer-executable program of instructions, such as programmodules, generally referred to as software applications or applicationprograms executed by any of a controller or the controller variationsdescribed herein. Software may include, in non-limiting examples,routines, programs, objects, components, and data structures thatperform particular tasks or implement particular data types. Thesoftware may form an interface to allow a computer to react according toa source of input. The software may also cooperate with other codesegments to initiate a variety of tasks in response to data received inconjunction with the source of the received data. The software may bestored on any of a variety of memory media, such as CD-ROM, magneticdisk, bubble memory, and semiconductor memory (e.g., various types ofRAM or ROM).

Moreover, aspects of the present disclosure may be practiced with avariety of computer-system and computer-network configurations,including multiprocessor systems, microprocessor-based orprogrammable-consumer electronics, minicomputers, mainframe computers,and the like. In addition, aspects of the present disclosure may bepracticed in distributed-computing environments where tasks areperformed by resident and remote-processing devices that are linkedthrough a communications network. In a distributed-computingenvironment, program modules may be located in both local and remotecomputer-storage media including memory storage devices. Aspects of thepresent disclosure may therefore be implemented in connection withvarious hardware, software or a combination thereof, in a computersystem or other processing system.

In one configuration, the various aspects of FIG. 1 may be implementedas executable code within a distributed, cloud-based computinginfrastructure that includes one or more server-class computing devicesoperative to manage the flow and processing of digital information. Theuser display, such as shown in FIG. 7 may be viewable on a user device,such as via a dedicated application or internet browser. The informationpopulated within the user display of FIG. 7 may be provided, forexample, by the server-class computing device used to implement FIG. 1 .It should be appreciated that other hardware or computing arrangementsmay be used to embody and/or implement the presently described systems.

Any of the methods described herein may include machine readableinstructions for execution by: (a) a processor, (b) a controller, and/or(c) any other suitable processing device. Any algorithm, software,control logic, protocol or method disclosed herein may be embodied assoftware stored on a tangible medium such as, for example, a flashmemory, a CD-ROM, a floppy disk, a hard drive, a digital versatile disk(DVD), or other memory devices. The entire algorithm, control logic,protocol, or method, and/or parts thereof, may alternatively be executedby a device other than a controller and/or embodied in firmware ordedicated hardware in an available manner (e.g., implemented by anapplication specific integrated circuit (ASIC), a programmable logicdevice (PLD), a field programmable logic device (FPLD), discrete logic,etc.). Further, although specific algorithms are described withreference to flowcharts depicted herein, many other methods forimplementing the example machine-readable instructions may alternativelybe used.

1. A multi-channel communication platform comprising: a messaging engineoperative to receive a plurality of textual messages from a user, theplurality of textual messages forming a message thread and including atleast one query requesting a reply; a routing engine operative totransmit the message thread to a message queue associated with a liveagent and to receive a reply from the live agent and transmit the replyto the user; a queue manager operative to provide the live agent with areward following a response to the query; and wherein the reward is avariable reward based on one or more of: the perceived complexity of thequery; a number of outstanding or unassigned queries; or the perceivedurgency of the query.
 2. The multi-channel communication platform ofclaim 1, wherein the queue manager is operative to specify a responsetime goal for providing the reply to the at least one query.
 3. Themulti-channel communication platform of claim 2, wherein the routingengine is further configured to determine a momentum of the plurality oftextual messages; wherein the momentum is a function of a mass and avelocity of the message thread, wherein the mass of the message threadis a measure of an urgency of the message thread, and wherein thevelocity is a measure of a speed at which the plurality of textualmessages are received.
 4. The multi-channel communication platform ofclaim 3, wherein the response time goal is inversely proportional to thedetermined momentum.
 5. The multi-channel communication platform ofclaim 3, wherein: the message queue comprises a plurality of messagethreads, each having a different momentum value; the message queue has amaximum momentum; and wherein a sum of the momentum value from each ofthe plurality of message threads in the message queue is less than themaximum momentum.
 6. The multi-channel communication platform of claim1, wherein the reward comprises a digital currency.
 7. The multi-channelcommunication platform of claim 6, further comprising: an avatarrepresenting the agent that is operative to be displayed to the user;and a digital reward store operative to provide a digital embellishmentfor display in conjunction with the avatar in exchange for the digitalcurrency.
 8. The multi-channel communication platform of claim 7,wherein the digital embellishment is uniquely secured to a token storedon a distributed blockchain ledger.
 9. The multi-channel communicationplatform of claim 1, wherein the live agent has an associated experiencescore; and wherein the routing engine is operative to analyze themessage thread to determine a required experience for responding to thequery; and wherein the routing engine is operative to transmit themessage thread to the message queue associated with the live agent ifthe experience score associated with the live agent is greater than thedetermined required experience for responding to the query.
 10. Themulti-channel communication platform of claim 1, wherein the routingengine selects the live agent from a pool of live agents.
 11. Themulti-channel communication platform of claim 10, wherein the pool oflive agents comprises a plurality of dedicated live agents and aplurality of on-demand live agents.
 12. A method for providing customerservice comprising: electronically receiving an inquiry from a user viaa messaging engine; analyzing the inquiry to determine an inquiryparameter selected from the group consisting of a complexity of theinquiry, a number of outstanding inquiries, and an urgency of theinquiry; adjusting a reward amount based on the inquiry parameter,wherein the reward amount increases as the complexity, number ofoutstanding inquiries, or urgency increases; offering the adjustedreward amount to a pool of agents; and assigning the inquiry to amessage queue of an agent from the pool of agents that accepts theoffered adjusted reward amount.
 13. The method of claim 12, wherein thepool of agents comprises a plurality of dedicated agents and a pluralityof on-demand agents.
 14. The method of claim 12, further comprising:determining an experience level required to respond to the inquiry; andoffering the adjusted reward amount only to agents in the pool of agentshaving at least the required experience level.
 15. The method of claim12, further comprising: receiving a response to the inquiry from theassigned agent; evaluating the response according to at least onebusiness promotion metric selected from the group consisting of apositive dispute resolution, encouragement of account signup,encouragement of account login, establishment of a relationship with theuser, getting the user to click a link, and making a sale; based on theevaluation, further adjusting the reward amount; and providing thefurther adjusted reward amount to the assigned agent.
 16. The method ofclaim 12, wherein the reward amount comprises a digital currency. 17.The method of claim 16, further comprising: enabling the assigned agentto exchange the digital currency for a digital item associated with anavatar representing the assigned agent.
 18. A system for providingcustomer service, comprising: a messaging engine operative to receive aplurality of textual messages from a user, the plurality of textualmessages forming a message thread and including at least one queryrequesting a reply; an analysis module configured to analyze the queryto determine an query parameter selected from the group consisting of acomplexity of the query, a number of outstanding queries, and an urgencyof the query; a pricing module configured to adjust a reward amountbased on the query parameter, wherein the reward amount increases as thecomplexity, number of outstanding queries, or urgency increases; anagent pool comprising a plurality of agents; a queue manager configuredto offer the adjusted reward amount to the agent pool and assign thequery to an agent from the agent pool that accepts the offered adjustedreward amount.
 19. The system of claim 18, wherein the agent poolcomprises a plurality of dedicated agents and a plurality of on-demandagents.
 20. The system of claim 18, further comprising: a responseevaluation module configured to: receive a response to the query fromthe assigned agent; evaluate the response according to at least onebusiness promotion metric selected from the group consisting of apositive dispute resolution, encouragement of account signup,encouragement of account login, establishment of a relationship with theuser, getting the user to click a link, and making a sale; and based onthe evaluation, adjust the reward amount; wherein the pricing module isfurther configured to provide the adjusted reward amount to the assignedagent.